Understanding Samples and Distributions

Flashcards covering key concepts related to sampling, distributions, and probabilities.

Law of Large Numbers

States that as the number of trials increases, the sample mean will converge to the population mean.

Random Sampling

A sampling method where each member of the population has an equal chance of being selected.

Simulation

A technique used to model the probability of various outcomes in a process that cannot easily be predicted due to the intervention of random variables.

Central Limit Theorem

States that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases.

Normal Distribution

A continuous probability distribution that is symmetrical about the mean, indicating that data near the mean are more frequent in occurrence.

Standard Normal Distribution

A normal distribution with a mean of 0 and a standard deviation of 1.

Z-score

A statistical measurement that describes a value's relation to the mean of a group of values, expressed in terms of standard deviations.

Cumulative Density Function (CDF)

A function that indicates the probability that a random variable takes a value less than or equal to a certain level.

Probability Density Function (PDF)

A function that describes the likelihood of a continuous random variable to take on a particular value.

Empirical Rule

In a normal distribution, roughly 68% of the data falls within one standard deviation of the mean, 95% within two standard deviations, and 99.7% within three standard deviations.